用数据增强提升非洲低资源语言翻译效果，最低提升25%性能
From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation
- 采用回译拼接与替换法增强语料
- 六种语言均实现至少25%的BLEU提升
- 适合关注非洲语言翻译的研究者
非洲大陆的语言多样性为机器翻译带来独特挑战与机遇。本研究探讨数据增强技术在改善低资源非洲语言翻译系统中的作用。针对六种非洲语言，采用句子拼接结合回译和词项替换两种增强方法。实验显示，所有语言的机器翻译性能显著提升，最小BLEU得分提升达25%。研究提供了全面分析，凸显这些技术在提升低资源语言翻译系统方面的潜力，有助于构建更鲁棒的少资源语言翻译体系。
原文摘要 · Abstract (English)
The linguistic diversity across the African continent presents different challenges and opportunities for machine translation. This study explores the effects of data augmentation techniques in improving translation systems in low-resource African languages. We focus on two data augmentation techniques: sentence concatenation with back translation and switch-out, applying them across six African languages. Our experiments show significant improvements in machine translation performance, with a minimum increase of 25\% in BLEU score across all six languages. We provide a comprehensive analysis and highlight the potential of these techniques to improve machine translation systems for low-resource languages, contributing to the development of more robust translation systems for under-resourced languages.
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